5,163 research outputs found
Constructing Permutation Rational Functions From Isogenies
A permutation rational function is a rational function
that induces a bijection on , that is, for all
there exists exactly one such that . Permutation
rational functions are intimately related to exceptional rational functions,
and more generally exceptional covers of the projective line, of which they
form the first important example.
In this paper, we show how to efficiently generate many permutation rational
functions over large finite fields using isogenies of elliptic curves, and
discuss some cryptographic applications. Our algorithm is based on Fried's
modular interpretation of certain dihedral exceptional covers of the projective
line (Cont. Math., 1994)
Counting Value Sets: Algorithm and Complexity
Let be a prime. Given a polynomial in \F_{p^m}[x] of degree over
the finite field \F_{p^m}, one can view it as a map from \F_{p^m} to
\F_{p^m}, and examine the image of this map, also known as the value set. In
this paper, we present the first non-trivial algorithm and the first complexity
result on computing the cardinality of this value set. We show an elementary
connection between this cardinality and the number of points on a family of
varieties in affine space. We then apply Lauder and Wan's -adic
point-counting algorithm to count these points, resulting in a non-trivial
algorithm for calculating the cardinality of the value set. The running time of
our algorithm is . In particular, this is a polynomial time
algorithm for fixed if is reasonably small. We also show that the
problem is #P-hard when the polynomial is given in a sparse representation,
, and is allowed to vary, or when the polynomial is given as a
straight-line program, and is allowed to vary. Additionally, we prove
that it is NP-hard to decide whether a polynomial represented by a
straight-line program has a root in a prime-order finite field, thus resolving
an open problem proposed by Kaltofen and Koiran in
\cite{Kaltofen03,KaltofenKo05}
Ramanujan Graphs in Polynomial Time
The recent work by Marcus, Spielman and Srivastava proves the existence of
bipartite Ramanujan (multi)graphs of all degrees and all sizes. However, that
paper did not provide a polynomial time algorithm to actually compute such
graphs. Here, we provide a polynomial time algorithm to compute certain
expected characteristic polynomials related to this construction. This leads to
a deterministic polynomial time algorithm to compute bipartite Ramanujan
(multi)graphs of all degrees and all sizes
Sparse multivariate polynomial interpolation in the basis of Schubert polynomials
Schubert polynomials were discovered by A. Lascoux and M. Sch\"utzenberger in
the study of cohomology rings of flag manifolds in 1980's. These polynomials
generalize Schur polynomials, and form a linear basis of multivariate
polynomials. In 2003, Lenart and Sottile introduced skew Schubert polynomials,
which generalize skew Schur polynomials, and expand in the Schubert basis with
the generalized Littlewood-Richardson coefficients.
In this paper we initiate the study of these two families of polynomials from
the perspective of computational complexity theory. We first observe that skew
Schubert polynomials, and therefore Schubert polynomials, are in \CountP
(when evaluating on non-negative integral inputs) and \VNP.
Our main result is a deterministic algorithm that computes the expansion of a
polynomial of degree in in the basis of Schubert
polynomials, assuming an oracle computing Schubert polynomials. This algorithm
runs in time polynomial in , , and the bit size of the expansion. This
generalizes, and derandomizes, the sparse interpolation algorithm of symmetric
polynomials in the Schur basis by Barvinok and Fomin (Advances in Applied
Mathematics, 18(3):271--285). In fact, our interpolation algorithm is general
enough to accommodate any linear basis satisfying certain natural properties.
Applications of the above results include a new algorithm that computes the
generalized Littlewood-Richardson coefficients.Comment: 20 pages; some typos correcte
A local construction of the Smith normal form of a matrix polynomial
We present an algorithm for computing a Smith form with multipliers of a
regular matrix polynomial over a field. This algorithm differs from previous
ones in that it computes a local Smith form for each irreducible factor in the
determinant separately and then combines them into a global Smith form, whereas
other algorithms apply a sequence of unimodular row and column operations to
the original matrix. The performance of the algorithm in exact arithmetic is
reported for several test cases.Comment: 26 pages, 6 figures; introduction expanded, 10 references added, two
additional tests performe
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